ipex-llm/python/llm/example/GPU/Long-Context/LLaMA2-32K/README.md

Llama2

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Llama2-32K models on Intel GPUs. For illustration purposes, we utilize the togethercomputer/Llama-2-7B-32K-Instruct as reference Llama2-32K models.

0. Requirements

To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.

Example: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a Llama2 model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations on Intel GPUs.

1. Install

1.1 Installation on Linux

We suggest using conda to manage environment:

conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

1.2 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.11 libuv
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

2. Configures OneAPI environment variables

2.1 Configurations for Linux

source /opt/intel/oneapi/setvars.sh

2.2 Configurations for Windows

call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"

Note: Please make sure you are using CMD (Anaconda Prompt if using conda) to run the command as PowerShell is not supported.

3. Runtime Configurations

For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.

3.1 Configurations for Linux

For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

For Intel Data Center GPU Max Series

export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export ENABLE_SDP_FUSION=1

Note: Please note that libtcmalloc.so can be installed by conda install -c conda-forge -y gperftools=2.10.

3.2 Configurations for Windows

For Intel iGPU

set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1

For Intel Arc™ A300-Series or Pro A60

set SYCL_CACHE_PERSISTENT=1

For other Intel dGPU Series

There is no need to set further environment variables.

Note: For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.

4. Running examples

4.1 Using simple prompt

python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Llama2 model (e.g. togethercomputer/Llama-2-7B-32K-Instruct) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'togethercomputer/Llama-2-7B-32K-Instruct'.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be 'What is AI?'.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 32.

4.2 Using 8k input size prompt

You can set the prompt argument to be a .txt file path containing the 8k size prompt text. An example command using the 8k input size prompt we provide is given below:

python ./generate.py --repo-id-or-model-path togethercomputer/Llama-2-7B-32K-Instruct --prompt 8k.txt

Note: If you need to use less memory, please set IPEX_LLM_LOW_MEM=1, which will enable memory optimization and may slightly affect the latency performance.

Sample Output

togethercomputer/Llama-2-7B-32K-Instruct

Inference time: xxxx s
-------------------- Prompt --------------------
<s>[INST] <<SYS>>

<</SYS>>

What is AI? [/INST]
-------------------- Output --------------------
[INST] <<SYS>>

<</SYS>>

What is AI? [/INST]

AI is a broad field of study that deals with the creation of intelligent agents, which are systems that can perform tasks that typically require human intelligence